First vs recurrent episode symptomatology in Major Depressive Disorder and its relation to brain function and structure: a network approach
Bibliographic record
Abstract
Abstract Background. Major Depressive Disorder (MDD) is a prevalent psychiatric disorder. At least half of the patients who recover from a first depressive episode, will experience a relapse. Therefore, understanding the underlying mechanisms supporting relapse is a clinical urgency that could be informed by studying complex brain-behavior associations. Here, we investigated how the relationships between depressive symptomatology and regional brain characteristics differed between people with first depressive episode vs recurrent depression. Methods. We used REST-meta-MDD data from the DIRECT consortium. We focused on comparing global and local network properties between first (n=239) and recurrent episode (n=179) on: (i) symptom network, (ii) brain structural (VBM) and functional networks (ALFF, ReHO), and (iii) integrated symptoms network and brain characteristics using the psychopathology and multimodal network approach. Results. Symptom network analysis showed high values of strength centrality for “Insomnia: Early Hours of the Morning” and “General somatic symptoms” at recurrence compared to the first episode. Also, differences in global strength in the integrated symptom-brain network (measured with ReHo metric) (S=2.09 p = 0.042). Finally, we found the edge of specific symptom-brain links, including insomnia and somatic symptoms-, to differ between the first episode and recurrence. Conclusions. For symptom networks, local but not global properties differentiated first from recurrent episode MDD, with specially stronger relations of insomnia and somatic symptoms in recurrent episode depression. For integrated symptom-brain networks, global strength of the network reflecting regional functional integrity (ReHO) was related to recurrence. This suggests that symptoms have relevance for understanding the complex brain-symptom relations underpinning recurrence of depression.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".